“But what about when ChatGPT gets 500000 times better?”
“OpenAI is going to eat your lunch”
“There’s not really any point in building anything when big tech AI will just stomp you out”
Okay. Let’s address this.
With the rapid rise of generative AI (huge fan), I’ve noticed a repeating conversation coming up again and again and again. I’m starting to call it the “AI doomsday” or the “lie-down-David” conversation, after David and Goliath.
The conversation usually goes something like this; AI (more specifically ChatGPT) will inevitably reach a singularity where it becomes so powerful it will be the omnipotent system that dominates the world for all and every scenario, leaving no room for competition.
What’s a poor entrepreneur to do?
The only feasible step forwards is to bet on businesses with moats outside of AI, since reliance on building differentiated AI alone is unsustainable. Valid concern, jumping to a few unfounded conclusions.
Let’s unpack.
Why ChatGPT Won’t Dominate Everything
Tools like ChatGPT are powerful, but they’re fundamentally generic.
Designed to answer any question or tackle any task, they excel at breadth but often struggle with depth. For example, crafting a lengthy educational experience tailored to an individual’s learning needs—knowing when to ask questions, repeat concepts, or gauge whether the learner has truly grasped something—requires a purpose-built mindset. Generic tools aren’t designed to specialize; they’re designed to generalize.
When AI becomes 50000000 times better, the same problem persists: context and specificity are still at risk of being lost. AI can be breathtakingly fast and capable, but without a clear focus or purpose, it risks becoming a jack-of-all-trades and master of none.
For industries like education, this is critical. Effective learning tools don’t just deliver answers; they adapt dynamically, guiding users through a process designed to ensure comprehension and growth. This level of focus and contextual awareness is something generic AI tools simply aren’t built for.
Context is a Real Moat
AI’s potential doesn’t lie in creating one tool to rule them all but in designing systems optimized for specific purposes. A tool tailored for learning, for instance, would have a singular goal: ensuring the user has truly learned something.
It would repeat concepts at the right time, ask clarifying questions, and adapt its teaching style to the learner. ChatGPT, brilliant as it is, lacks this educational focus. It doesn’t ask, “Did you learn something?”—and more importantly, it doesn’t care.
This underscores why specialization remains crucial, even as AI capabilities grow. The real moat isn’t in betting on AI’s generic potential but in investing in AI that excels at solving highly specific problems.
Examples
Consider the healthcare industry. Tools like PathAI are designed specifically to assist pathologists in diagnosing diseases from medical images. While a generalist AI could analyze images, PathAI excels because it’s trained on domain-specific data, optimized for medical decision-making, and tailored to support professionals in a high-stakes environment. This specialization enables it to outperform general-purpose tools like ChatGPT, which lack the contextual depth required for accurate diagnoses.
Similarly, in education, Duolingo leverages AI tailored for language learning. Its algorithms adjust lesson difficulty, repetition, and pacing based on user performance, creating an experience designed specifically to help users acquire a new language. ChatGPT, while capable of generating language exercises, doesn’t have this level of tailored feedback and progression tracking.
Rather than fearing an AI singularity where one tool dominates all industries, the focus should shift to creating purpose-driven AI that thrives in specific domains. Whether it’s learning, healthcare, or logistics, AI systems tailored to a single mission will always outperform generalist tools in delivering meaningful results.
The lesson for the AI doomsday conversation? The future of AI isn’t about a single dominant force. It’s about ecosystems of specialized tools solving problems with precision. Context, focus, and purpose are the real game-changers—and the moats worth building.
